python - 在 Azure Databricks 中按范围内的值进行分组

Question

考虑以下数据:

EventDate,Value
1.1.2019,11
1.2.2019,5
1.3.2019,6
1.4.2019,-15
1.5.2019,-20
1.6.2019,-30
1.7.2019,12
1.8.2019,20

我想创建当这些值在阈值内时的组:

 1. > 10
 2. <=10 >=-10
 3. >-10

结果应该是值的开始和结束处于某种状态:

1.1.2019, 1.1.2019, [11]
1.2.2019, 1.3.2019, [5, 6]
1.4.2019, 1.6.2019, [-15, -20, -30]
1.7.2019, 1.8.2018, [12, 20]

我相信答案就在窗口函数内，但我对 databricks 相当陌生，我还不明白如何使用它。

这是一个基于将数据帧作为列表循环的工作(python)解决方案，但是我更喜欢直接在数据帧上工作的解决方案以提高性能。

from pyspark.sql.functions import *
import pandas as pd
STATETHRESHOLDCHARGE = 10
list = [{"eventDateTime":x["EventDate"], "value":x["Value"]} for x in dataframe.sort(dfArrayOneCast.EventDate).rdd.collect()]
cycles = []
previous = None
for row in list:
  currentState = 'charge'
  if row["value"] < STATETHRESHOLDCHARGE and row["value"] > (STATETHRESHOLDCHARGE * -1):
    currentState = 'idle'
  if row["value"] <= (STATETHRESHOLDCHARGE * -1):
    currentState = 'discharge'

  eventDateTime = row["eventDateTime"]
  if previous is None or previous["state"] != currentState:
    previous = {"start":row["eventDateTime"], "end":row["eventDateTime"], "values":[row["value"]], "timestamps":[row["eventDateTime"]], "state":currentState}
    cycles.append(previous)
  else:
    previous["end"] = row["eventDateTime"]
    previous["values"].append(row["value"])
    previous["timestamps"].append(row["eventDateTime"])

display(cycles)

Answer 1

假设 df 数据框中有上述数据，让我们逐条分析

from pyspark.sql.functions import col, last, lag, udf, when, collect_list
from pyspark.sql.types import StringType
value = 'value'
date = 'EventDate'
valueBag = 'valueBag'

def bagTransform(v):
  if v > 10:
    return 'charging'
  elif v < -10:
    return 'discharging'
  else:
    return 'idle'

bagTransformUDF = udf(bagTransform, StringType())  

withBaggedValue = df.withColumn(valueBag, bagTransformUDF(col(value)))

因此，首先我们将值放入您声明的范围中，现在我们可以使用 lag 将窗口移动到先前的值:

from pyspark.sql import Window
windowSpec = Window.orderBy(date)
prevValueBag = 'prevValueBag'
bagBeginning = 'bagBeginning'

withLag = (withBaggedValue
  .withColumn(prevValueBag, lag(withBaggedValue[valueBag]).over(windowSpec)))

现在有趣的部分开始:我们检测变化点并临时分配当前事件日期或 null:

withInitialBeginnings = withLag.withColumn(bagBeginning, when((col(prevValueBag) != col(valueBag)) | col(prevValueBag).isNull(), col(date)).otherwise(None))

并使用最后找到的值填写它们

withFilledBeginnings = (withInitialBeginnings.withColumn(bagBeginning, 
                 last(col(bagBeginning), ignorenulls=True)
                 .over(windowSpec)))
display(withFilledBeginnings)

有了这个集合，我们就可以简单地聚合起始点

aggregate = withFilledBeginnings.groupby(col(bagBeginning)).agg(collect_list(value))

display(aggregate)

如果您还需要结束日期，您可以使用 pyspark.sql.functions.lead 进行类似的预处理，该预处理与 last 对称但向前。